Data Science Dojo
JULY 6, 2023
These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Apache Hadoop: Apache Hadoop is an open-source framework for distributed storage and processing of large datasets.
Pickl AI
JULY 25, 2023
They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.
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Smart Data Collective
MAY 20, 2019
For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. SQL programming skills, specific tool experience — Tableau for example — and problem-solving are just a handful of examples.
Mlearning.ai
JULY 10, 2023
In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Views Views in GCP BigQuery are virtual tables defined by SQL query that can display the results of a query or be used as the base for other queries.
IBM Journey to AI blog
JANUARY 18, 2023
While traditional data warehouses made use of an Extract-Transform-Load (ETL) process to ingest data, data lakes instead rely on an Extract-Load-Transform (ELT) process. This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. All phases of the data-information lifecycle.
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